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@shubsraj shubsraj commented May 8, 2025

…ured.

Description

A progress bar has been added for training and validation processes, allowing better monitoring of training.

  • progress bar using tqdm

How has this change been tested, please provide a testcase or example of how you tested the change?

YOUR_ANSWER

no special change, just created a progress bar using tqdm and implemented in the main engine code for one epoch and the evaluate function, which adds structure to the logs and making it easier to read.

For example, documentation changes, usability, usage/costs, secrets, etc.

Docs

No change in the documentation
Screenshot 2025-05-08 232538

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CLAassistant commented May 8, 2025

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…passing issue when only evaluating any pretrained model.
@nok
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nok commented May 25, 2025

Hi @shubsraj , I created a new PR shubsraj#1 which mainly adds a config parameter progress_bar to toggle the progress bar. In addition I change the color of the progress bar (see below). And I tested the progress bar with multiple gpus successfully.

Screenshot 2025-05-26 at 00 36 25

Add config parameter `progress_bar`

print("Grad accum steps: ", args.grad_accum_steps)
print("Total batch size: ", batch_size * utils.get_world_size())
# print("Grad accum steps: ", args.grad_accum_steps)
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I'm not sure how helpful these print statements are in the current context, so I left them unchanged. I'll leave it to a core maintainer to decide whether they should be removed or kept.

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Every time before an epoch, the same info was printed, so I shifted it to the main code to print it once, before the start of the training while adding a progress bar.

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3 participants